Overview

Dataset statistics

Number of variables27
Number of observations55692
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 MiB
Average record size in memory216.0 B

Variable types

Numeric20
Categorical5
Boolean2

Alerts

oral has constant value ""Constant
hearing(left) is highly imbalanced (82.8%)Imbalance
hearing(right) is highly imbalanced (82.5%)Imbalance
AST is highly skewed (γ1 = 25.14752832)Skewed
ALT is highly skewed (γ1 = 34.68679686)Skewed
ID is uniformly distributedUniform
ID has unique valuesUnique

Reproduction

Analysis started2024-04-03 18:34:17.427108
Analysis finished2024-04-03 18:34:38.669500
Duration21.24 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct55692
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27845.5
Minimum0
Maximum55691
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:38.706887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2784.55
Q113922.75
median27845.5
Q341768.25
95-th percentile52906.45
Maximum55691
Range55691
Interquartile range (IQR)27845.5

Descriptive statistics

Standard deviation16077.04
Coefficient of variation (CV)0.57736582
Kurtosis-1.2
Mean27845.5
Median Absolute Deviation (MAD)13923
Skewness-8.642558 × 10-17
Sum1.5507716 × 109
Variance2.5847121 × 108
MonotonicityNot monotonic
2024-04-03T12:34:38.757552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
46400 1
 
< 0.1%
46387 1
 
< 0.1%
46388 1
 
< 0.1%
46389 1
 
< 0.1%
46390 1
 
< 0.1%
46391 1
 
< 0.1%
46392 1
 
< 0.1%
46394 1
 
< 0.1%
46395 1
 
< 0.1%
Other values (55682) 55682
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
55691 1
< 0.1%
55690 1
< 0.1%
55689 1
< 0.1%
55688 1
< 0.1%
55687 1
< 0.1%
55686 1
< 0.1%
55685 1
< 0.1%
55684 1
< 0.1%
55683 1
< 0.1%
55682 1
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.2 KiB
M
35401 
F
20291 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters55692
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 35401
63.6%
F 20291
36.4%

Length

2024-04-03T12:34:38.803239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T12:34:38.842518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
m 35401
63.6%
f 20291
36.4%

Most occurring characters

ValueCountFrequency (%)
M 35401
63.6%
F 20291
36.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 55692
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 35401
63.6%
F 20291
36.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 55692
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 35401
63.6%
F 20291
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 35401
63.6%
F 20291
36.4%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.182917
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:38.880210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q140
median40
Q355
95-th percentile65
Maximum85
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.071418
Coefficient of variation (CV)0.27321459
Kurtosis-0.15644929
Mean44.182917
Median Absolute Deviation (MAD)10
Skewness0.26805347
Sum2460635
Variance145.71912
MonotonicityNot monotonic
2024-04-03T12:34:38.920451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 15181
27.3%
45 7037
12.6%
60 6167
11.1%
50 5549
 
10.0%
55 5020
 
9.0%
35 4480
 
8.0%
30 4056
 
7.3%
25 3525
 
6.3%
20 1605
 
2.9%
65 1336
 
2.4%
Other values (4) 1736
 
3.1%
ValueCountFrequency (%)
20 1605
 
2.9%
25 3525
 
6.3%
30 4056
 
7.3%
35 4480
 
8.0%
40 15181
27.3%
45 7037
12.6%
50 5549
 
10.0%
55 5020
 
9.0%
60 6167
11.1%
65 1336
 
2.4%
ValueCountFrequency (%)
85 15
 
< 0.1%
80 280
 
0.5%
75 614
 
1.1%
70 827
 
1.5%
65 1336
 
2.4%
60 6167
11.1%
55 5020
 
9.0%
50 5549
 
10.0%
45 7037
12.6%
40 15181
27.3%

height(cm)
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.64932
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:38.957978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile150
Q1160
median165
Q3170
95-th percentile180
Maximum190
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1945969
Coefficient of variation (CV)0.055843515
Kurtosis-0.60969868
Mean164.64932
Median Absolute Deviation (MAD)5
Skewness-0.1422381
Sum9169650
Variance84.540612
MonotonicityNot monotonic
2024-04-03T12:34:38.995194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
170 11381
20.4%
165 9949
17.9%
160 8919
16.0%
175 8009
14.4%
155 7627
13.7%
150 4492
 
8.1%
180 3149
 
5.7%
145 1236
 
2.2%
185 681
 
1.2%
140 205
 
0.4%
Other values (3) 44
 
0.1%
ValueCountFrequency (%)
130 1
 
< 0.1%
135 6
 
< 0.1%
140 205
 
0.4%
145 1236
 
2.2%
150 4492
 
8.1%
155 7627
13.7%
160 8919
16.0%
165 9949
17.9%
170 11381
20.4%
175 8009
14.4%
ValueCountFrequency (%)
190 37
 
0.1%
185 681
 
1.2%
180 3149
 
5.7%
175 8009
14.4%
170 11381
20.4%
165 9949
17.9%
160 8919
16.0%
155 7627
13.7%
150 4492
 
8.1%
145 1236
 
2.2%

weight(kg)
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.864936
Minimum30
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:39.041592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile45
Q155
median65
Q375
95-th percentile90
Maximum135
Range105
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.820306
Coefficient of variation (CV)0.19464538
Kurtosis0.30261342
Mean65.864936
Median Absolute Deviation (MAD)10
Skewness0.53404231
Sum3668150
Variance164.36024
MonotonicityNot monotonic
2024-04-03T12:34:39.084383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
65 8196
14.7%
60 8139
14.6%
70 7722
13.9%
55 7326
13.2%
75 6088
10.9%
50 5564
10.0%
80 4117
7.4%
85 2529
 
4.5%
45 2370
 
4.3%
90 1477
 
2.7%
Other values (12) 2164
 
3.9%
ValueCountFrequency (%)
30 7
 
< 0.1%
35 39
 
0.1%
40 468
 
0.8%
45 2370
 
4.3%
50 5564
10.0%
55 7326
13.2%
60 8139
14.6%
65 8196
14.7%
70 7722
13.9%
75 6088
10.9%
ValueCountFrequency (%)
135 1
 
< 0.1%
130 4
 
< 0.1%
125 8
 
< 0.1%
120 23
 
< 0.1%
115 49
 
0.1%
110 113
 
0.2%
105 200
 
0.4%
100 436
 
0.8%
95 816
1.5%
90 1477
2.7%

waist(cm)
Real number (ℝ)

Distinct566
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.046418
Minimum51
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:39.134889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile67
Q176
median82
Q388
95-th percentile98
Maximum129
Range78
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.2742228
Coefficient of variation (CV)0.11303629
Kurtosis0.1343937
Mean82.046418
Median Absolute Deviation (MAD)6
Skewness0.24239007
Sum4569329.1
Variance86.011208
MonotonicityNot monotonic
2024-04-03T12:34:39.191915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 1917
 
3.4%
82 1775
 
3.2%
81 1737
 
3.1%
84 1695
 
3.0%
78 1667
 
3.0%
86 1619
 
2.9%
85 1610
 
2.9%
83 1593
 
2.9%
79 1515
 
2.7%
76 1506
 
2.7%
Other values (556) 39058
70.1%
ValueCountFrequency (%)
51 2
 
< 0.1%
53 1
 
< 0.1%
54 2
 
< 0.1%
55 5
< 0.1%
56 5
< 0.1%
56.2 2
 
< 0.1%
56.4 1
 
< 0.1%
56.6 1
 
< 0.1%
57 8
< 0.1%
57.2 1
 
< 0.1%
ValueCountFrequency (%)
129 1
 
< 0.1%
128 1
 
< 0.1%
127.7 1
 
< 0.1%
127 2
< 0.1%
125.8 2
< 0.1%
124.4 1
 
< 0.1%
124 3
< 0.1%
123 1
 
< 0.1%
122.5 1
 
< 0.1%
122 1
 
< 0.1%

eyesight(left)
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.012623
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:39.239264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.48687328
Coefficient of variation (CV)0.48080409
Kurtosis181.06098
Mean1.012623
Median Absolute Deviation (MAD)0.2
Skewness9.9876508
Sum56395
Variance0.23704559
MonotonicityNot monotonic
2024-04-03T12:34:39.288520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.2 12746
22.9%
1 12217
21.9%
1.5 7825
14.1%
0.8 5267
9.5%
0.9 5125
9.2%
0.7 4445
 
8.0%
0.6 2508
 
4.5%
0.5 2113
 
3.8%
0.4 1218
 
2.2%
0.3 881
 
1.6%
Other values (9) 1347
 
2.4%
ValueCountFrequency (%)
0.1 354
 
0.6%
0.2 464
 
0.8%
0.3 881
 
1.6%
0.4 1218
 
2.2%
0.5 2113
 
3.8%
0.6 2508
 
4.5%
0.7 4445
 
8.0%
0.8 5267
9.5%
0.9 5125
9.2%
1 12217
21.9%
ValueCountFrequency (%)
9.9 92
 
0.2%
2 401
 
0.7%
1.9 2
 
< 0.1%
1.8 1
 
< 0.1%
1.6 20
 
< 0.1%
1.5 7825
14.1%
1.3 10
 
< 0.1%
1.2 12746
22.9%
1.1 3
 
< 0.1%
1 12217
21.9%

eyesight(right)
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0074427
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:39.329230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.48596441
Coefficient of variation (CV)0.48237423
Kurtosis182.86838
Mean1.0074427
Median Absolute Deviation (MAD)0.2
Skewness10.059531
Sum56106.5
Variance0.23616141
MonotonicityNot monotonic
2024-04-03T12:34:39.480336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1.2 12539
22.5%
1 12498
22.4%
1.5 7536
13.5%
0.8 5418
9.7%
0.9 5277
9.5%
0.7 4325
 
7.8%
0.6 2402
 
4.3%
0.5 2160
 
3.9%
0.4 1309
 
2.4%
0.3 842
 
1.5%
Other values (7) 1386
 
2.5%
ValueCountFrequency (%)
0.1 366
 
0.7%
0.2 522
 
0.9%
0.3 842
 
1.5%
0.4 1309
 
2.4%
0.5 2160
 
3.9%
0.6 2402
 
4.3%
0.7 4325
 
7.8%
0.8 5418
9.7%
0.9 5277
9.5%
1 12498
22.4%
ValueCountFrequency (%)
9.9 92
 
0.2%
2 377
 
0.7%
1.6 20
 
< 0.1%
1.5 7536
13.5%
1.3 7
 
< 0.1%
1.2 12539
22.5%
1.1 2
 
< 0.1%
1 12498
22.4%
0.9 5277
9.5%
0.8 5418
9.7%

hearing(left)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.2 KiB
1.0
54267 
2.0
 
1425

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters167076
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 54267
97.4%
2.0 1425
 
2.6%

Length

2024-04-03T12:34:39.519420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T12:34:39.551619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 54267
97.4%
2.0 1425
 
2.6%

Most occurring characters

ValueCountFrequency (%)
. 55692
33.3%
0 55692
33.3%
1 54267
32.5%
2 1425
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111384
66.7%
Other Punctuation 55692
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55692
50.0%
1 54267
48.7%
2 1425
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 55692
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 167076
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 55692
33.3%
0 55692
33.3%
1 54267
32.5%
2 1425
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 55692
33.3%
0 55692
33.3%
1 54267
32.5%
2 1425
 
0.9%

hearing(right)
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.2 KiB
1.0
54236 
2.0
 
1456

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters167076
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 54236
97.4%
2.0 1456
 
2.6%

Length

2024-04-03T12:34:39.587584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T12:34:39.620355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 54236
97.4%
2.0 1456
 
2.6%

Most occurring characters

ValueCountFrequency (%)
. 55692
33.3%
0 55692
33.3%
1 54236
32.5%
2 1456
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111384
66.7%
Other Punctuation 55692
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55692
50.0%
1 54236
48.7%
2 1456
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 55692
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 167076
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 55692
33.3%
0 55692
33.3%
1 54236
32.5%
2 1456
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 55692
33.3%
0 55692
33.3%
1 54236
32.5%
2 1456
 
0.9%

systolic
Real number (ℝ)

Distinct130
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.49422
Minimum71
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:39.659916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile100
Q1112
median120
Q3130
95-th percentile144
Maximum240
Range169
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.675989
Coefficient of variation (CV)0.11256494
Kurtosis1.3061712
Mean121.49422
Median Absolute Deviation (MAD)10
Skewness0.46977958
Sum6766256
Variance187.03268
MonotonicityNot monotonic
2024-04-03T12:34:39.717022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 3490
 
6.3%
120 3427
 
6.2%
130 3273
 
5.9%
118 2992
 
5.4%
124 1543
 
2.8%
128 1538
 
2.8%
116 1528
 
2.7%
119 1507
 
2.7%
100 1485
 
2.7%
122 1478
 
2.7%
Other values (120) 33431
60.0%
ValueCountFrequency (%)
71 1
 
< 0.1%
72 1
 
< 0.1%
74 1
 
< 0.1%
79 2
 
< 0.1%
80 3
 
< 0.1%
81 7
< 0.1%
82 7
< 0.1%
83 6
< 0.1%
84 8
< 0.1%
85 3
 
< 0.1%
ValueCountFrequency (%)
240 1
 
< 0.1%
233 1
 
< 0.1%
223 1
 
< 0.1%
220 1
 
< 0.1%
213 1
 
< 0.1%
208 1
 
< 0.1%
204 3
< 0.1%
203 3
< 0.1%
200 2
 
< 0.1%
199 5
< 0.1%

relaxation
Real number (ℝ)

Distinct95
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.00483
Minimum40
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:39.817692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q170
median76
Q382
95-th percentile91
Maximum146
Range106
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.6792778
Coefficient of variation (CV)0.12735082
Kurtosis0.96270735
Mean76.00483
Median Absolute Deviation (MAD)6
Skewness0.39460244
Sum4232861
Variance93.688418
MonotonicityNot monotonic
2024-04-03T12:34:39.879493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 5435
 
9.8%
70 5226
 
9.4%
78 3183
 
5.7%
60 2182
 
3.9%
72 2159
 
3.9%
74 2103
 
3.8%
76 2040
 
3.7%
75 1854
 
3.3%
82 1769
 
3.2%
84 1715
 
3.1%
Other values (85) 28026
50.3%
ValueCountFrequency (%)
40 5
 
< 0.1%
42 1
 
< 0.1%
44 3
 
< 0.1%
45 1
 
< 0.1%
46 5
 
< 0.1%
47 3
 
< 0.1%
48 10
 
< 0.1%
49 8
 
< 0.1%
50 34
0.1%
51 58
0.1%
ValueCountFrequency (%)
146 3
< 0.1%
140 2
< 0.1%
137 1
 
< 0.1%
136 2
< 0.1%
134 1
 
< 0.1%
133 2
< 0.1%
132 2
< 0.1%
130 2
< 0.1%
129 1
 
< 0.1%
128 3
< 0.1%

fasting blood sugar
Real number (ℝ)

Distinct276
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.312325
Minimum46
Maximum505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:39.929430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile80
Q189
median96
Q3104
95-th percentile130
Maximum505
Range459
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.795591
Coefficient of variation (CV)0.20939588
Kurtosis36.299052
Mean99.312325
Median Absolute Deviation (MAD)7
Skewness4.5094116
Sum5530902
Variance432.45662
MonotonicityNot monotonic
2024-04-03T12:34:39.978233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 2213
 
4.0%
97 2151
 
3.9%
93 2141
 
3.8%
92 2133
 
3.8%
95 2132
 
3.8%
91 2098
 
3.8%
98 2025
 
3.6%
96 1993
 
3.6%
90 1984
 
3.6%
99 1962
 
3.5%
Other values (266) 34860
62.6%
ValueCountFrequency (%)
46 2
 
< 0.1%
48 1
 
< 0.1%
51 1
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
56 3
 
< 0.1%
57 3
 
< 0.1%
58 1
 
< 0.1%
59 5
< 0.1%
60 10
< 0.1%
ValueCountFrequency (%)
505 1
 
< 0.1%
475 1
 
< 0.1%
423 1
 
< 0.1%
398 2
< 0.1%
391 1
 
< 0.1%
386 1
 
< 0.1%
375 2
< 0.1%
369 4
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%

Cholesterol
Real number (ℝ)

Distinct286
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.90142
Minimum55
Maximum445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.027461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile141
Q1172
median195
Q3220
95-th percentile259
Maximum445
Range390
Interquartile range (IQR)48

Descriptive statistics

Standard deviation36.29794
Coefficient of variation (CV)0.18434575
Kurtosis0.60506769
Mean196.90142
Median Absolute Deviation (MAD)24
Skewness0.39235544
Sum10965834
Variance1317.5405
MonotonicityNot monotonic
2024-04-03T12:34:40.082641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 696
 
1.2%
192 664
 
1.2%
198 654
 
1.2%
187 650
 
1.2%
189 639
 
1.1%
197 639
 
1.1%
188 638
 
1.1%
178 637
 
1.1%
190 634
 
1.1%
195 634
 
1.1%
Other values (276) 49207
88.4%
ValueCountFrequency (%)
55 1
 
< 0.1%
72 1
 
< 0.1%
77 2
 
< 0.1%
84 1
 
< 0.1%
86 2
 
< 0.1%
87 1
 
< 0.1%
90 3
< 0.1%
91 6
< 0.1%
92 4
< 0.1%
93 5
< 0.1%
ValueCountFrequency (%)
445 1
< 0.1%
442 1
< 0.1%
441 1
< 0.1%
419 1
< 0.1%
410 1
< 0.1%
406 1
< 0.1%
395 1
< 0.1%
393 2
< 0.1%
386 1
< 0.1%
380 2
< 0.1%

triglyceride
Real number (ℝ)

Distinct390
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.6657
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.136944image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q174
median108
Q3160
95-th percentile278
Maximum999
Range991
Interquartile range (IQR)86

Descriptive statistics

Standard deviation71.639817
Coefficient of variation (CV)0.56558183
Kurtosis1.8916938
Mean126.6657
Median Absolute Deviation (MAD)39
Skewness1.3134029
Sum7054266
Variance5132.2634
MonotonicityNot monotonic
2024-04-03T12:34:40.187046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 524
 
0.9%
82 498
 
0.9%
79 494
 
0.9%
83 486
 
0.9%
85 477
 
0.9%
67 464
 
0.8%
78 462
 
0.8%
80 461
 
0.8%
69 460
 
0.8%
59 457
 
0.8%
Other values (380) 50909
91.4%
ValueCountFrequency (%)
8 1
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
16 5
 
< 0.1%
19 3
 
< 0.1%
20 8
< 0.1%
21 10
< 0.1%
22 8
< 0.1%
23 10
< 0.1%
24 15
< 0.1%
ValueCountFrequency (%)
999 1
 
< 0.1%
548 1
 
< 0.1%
466 2
 
< 0.1%
432 2
 
< 0.1%
405 1
 
< 0.1%
399 17
< 0.1%
398 9
< 0.1%
397 21
< 0.1%
396 6
 
< 0.1%
395 11
< 0.1%

HDL
Real number (ℝ)

Distinct126
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.290347
Minimum4
Maximum618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.235553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile37
Q147
median55
Q366
95-th percentile84
Maximum618
Range614
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.738963
Coefficient of variation (CV)0.25726782
Kurtosis41.771658
Mean57.290347
Median Absolute Deviation (MAD)9
Skewness1.955282
Sum3190614
Variance217.23702
MonotonicityNot monotonic
2024-04-03T12:34:40.285775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 1686
 
3.0%
51 1678
 
3.0%
50 1657
 
3.0%
56 1643
 
3.0%
53 1640
 
2.9%
55 1624
 
2.9%
49 1610
 
2.9%
47 1603
 
2.9%
48 1583
 
2.8%
52 1576
 
2.8%
Other values (116) 39392
70.7%
ValueCountFrequency (%)
4 3
 
< 0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
17 2
 
< 0.1%
18 4
 
< 0.1%
21 2
 
< 0.1%
22 3
 
< 0.1%
23 5
 
< 0.1%
24 11
< 0.1%
25 15
< 0.1%
ValueCountFrequency (%)
618 1
 
< 0.1%
359 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
155 1
 
< 0.1%
148 2
 
< 0.1%
144 1
 
< 0.1%
136 2
 
< 0.1%
135 2
 
< 0.1%
133 5
< 0.1%

LDL
Real number (ℝ)

Distinct289
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.9645
Minimum1
Maximum1860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.336200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62.55
Q192
median113
Q3136
95-th percentile171
Maximum1860
Range1859
Interquartile range (IQR)44

Descriptive statistics

Standard deviation40.926476
Coefficient of variation (CV)0.35599229
Kurtosis352.10735
Mean114.9645
Median Absolute Deviation (MAD)22
Skewness10.673511
Sum6402603
Variance1674.9765
MonotonicityNot monotonic
2024-04-03T12:34:40.386459image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 727
 
1.3%
112 726
 
1.3%
107 703
 
1.3%
121 702
 
1.3%
106 694
 
1.2%
111 693
 
1.2%
101 683
 
1.2%
116 682
 
1.2%
114 680
 
1.2%
96 679
 
1.2%
Other values (279) 48723
87.5%
ValueCountFrequency (%)
1 4
< 0.1%
4 1
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
12 5
< 0.1%
13 4
< 0.1%
14 1
 
< 0.1%
15 4
< 0.1%
ValueCountFrequency (%)
1860 1
< 0.1%
1810 1
< 0.1%
1660 2
< 0.1%
1600 1
< 0.1%
1560 1
< 0.1%
1400 1
< 0.1%
1340 1
< 0.1%
1260 1
< 0.1%
1220 1
< 0.1%
1200 2
< 0.1%

hemoglobin
Real number (ℝ)

Distinct145
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.622592
Minimum4.9
Maximum21.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.436312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile12.1
Q113.6
median14.8
Q315.8
95-th percentile16.9
Maximum21.1
Range16.2
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.5644984
Coefficient of variation (CV)0.10699187
Kurtosis1.2124322
Mean14.622592
Median Absolute Deviation (MAD)1.1
Skewness-0.65523701
Sum814361.4
Variance2.4476554
MonotonicityNot monotonic
2024-04-03T12:34:40.487686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 1523
 
2.7%
15.4 1522
 
2.7%
15.6 1497
 
2.7%
15.3 1492
 
2.7%
15.5 1475
 
2.6%
15.7 1459
 
2.6%
15.8 1408
 
2.5%
15.2 1401
 
2.5%
14.9 1393
 
2.5%
15.1 1361
 
2.4%
Other values (135) 41161
73.9%
ValueCountFrequency (%)
4.9 2
 
< 0.1%
5 2
 
< 0.1%
5.5 2
 
< 0.1%
5.8 2
 
< 0.1%
5.9 1
 
< 0.1%
6.1 1
 
< 0.1%
6.2 1
 
< 0.1%
6.3 5
< 0.1%
6.4 1
 
< 0.1%
6.6 3
< 0.1%
ValueCountFrequency (%)
21.1 1
 
< 0.1%
20.9 2
< 0.1%
20.4 1
 
< 0.1%
20 2
< 0.1%
19.8 1
 
< 0.1%
19.7 2
< 0.1%
19.6 2
< 0.1%
19.5 1
 
< 0.1%
19.4 1
 
< 0.1%
19.3 4
< 0.1%

Urine protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0872118
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.530442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4048824
Coefficient of variation (CV)0.37240435
Kurtosis36.427311
Mean1.0872118
Median Absolute Deviation (MAD)0
Skewness5.6250876
Sum60549
Variance0.16392976
MonotonicityNot monotonic
2024-04-03T12:34:40.568926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 52599
94.4%
2 1795
 
3.2%
3 940
 
1.7%
4 260
 
0.5%
5 88
 
0.2%
6 10
 
< 0.1%
ValueCountFrequency (%)
1 52599
94.4%
2 1795
 
3.2%
3 940
 
1.7%
4 260
 
0.5%
5 88
 
0.2%
6 10
 
< 0.1%
ValueCountFrequency (%)
6 10
 
< 0.1%
5 88
 
0.2%
4 260
 
0.5%
3 940
 
1.7%
2 1795
 
3.2%
1 52599
94.4%

serum creatinine
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88573763
Minimum0.1
Maximum11.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.610057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.8
median0.9
Q31
95-th percentile1.2
Maximum11.6
Range11.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.22152414
Coefficient of variation (CV)0.25010131
Kurtosis364.44336
Mean0.88573763
Median Absolute Deviation (MAD)0.1
Skewness9.4019276
Sum49328.5
Variance0.049072945
MonotonicityNot monotonic
2024-04-03T12:34:40.664952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.9 11293
20.3%
0.8 10508
18.9%
1 9718
17.4%
0.7 7465
13.4%
1.1 6161
11.1%
0.6 4493
 
8.1%
1.2 2907
 
5.2%
0.5 1493
 
2.7%
1.3 898
 
1.6%
1.4 292
 
0.5%
Other values (28) 464
 
0.8%
ValueCountFrequency (%)
0.1 23
 
< 0.1%
0.2 2
 
< 0.1%
0.3 15
 
< 0.1%
0.4 206
 
0.4%
0.5 1493
 
2.7%
0.6 4493
 
8.1%
0.7 7465
13.4%
0.8 10508
18.9%
0.9 11293
20.3%
1 9718
17.4%
ValueCountFrequency (%)
11.6 1
< 0.1%
10.3 1
< 0.1%
10 2
< 0.1%
9.9 1
< 0.1%
7.5 1
< 0.1%
7.4 2
< 0.1%
6.4 1
< 0.1%
5.9 1
< 0.1%
5 1
< 0.1%
3.4 2
< 0.1%

AST
Real number (ℝ)

SKEWED 

Distinct219
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.182935
Minimum6
Maximum1311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.717638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15
Q119
median23
Q328
95-th percentile45
Maximum1311
Range1305
Interquartile range (IQR)9

Descriptive statistics

Standard deviation19.35546
Coefficient of variation (CV)0.73923951
Kurtosis1182.1924
Mean26.182935
Median Absolute Deviation (MAD)4
Skewness25.147528
Sum1458180
Variance374.63382
MonotonicityNot monotonic
2024-04-03T12:34:40.777502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 3817
 
6.9%
21 3748
 
6.7%
22 3609
 
6.5%
19 3575
 
6.4%
23 3375
 
6.1%
18 3239
 
5.8%
24 3158
 
5.7%
25 2728
 
4.9%
17 2697
 
4.8%
26 2305
 
4.1%
Other values (209) 23441
42.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 3
 
< 0.1%
8 3
 
< 0.1%
9 25
 
< 0.1%
10 47
 
0.1%
11 107
 
0.2%
12 281
 
0.5%
13 555
 
1.0%
14 971
1.7%
15 1592
2.9%
ValueCountFrequency (%)
1311 1
< 0.1%
1090 2
< 0.1%
981 2
< 0.1%
976 1
< 0.1%
841 1
< 0.1%
778 1
< 0.1%
656 1
< 0.1%
591 2
< 0.1%
545 1
< 0.1%
527 1
< 0.1%

ALT
Real number (ℝ)

SKEWED 

Distinct245
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.036037
Minimum1
Maximum2914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.829667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median21
Q331
95-th percentile62
Maximum2914
Range2913
Interquartile range (IQR)16

Descriptive statistics

Standard deviation30.947853
Coefficient of variation (CV)1.1446889
Kurtosis2331.6475
Mean27.036037
Median Absolute Deviation (MAD)7
Skewness34.686797
Sum1505691
Variance957.7696
MonotonicityNot monotonic
2024-04-03T12:34:40.890868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2744
 
4.9%
16 2621
 
4.7%
17 2621
 
4.7%
14 2576
 
4.6%
18 2544
 
4.6%
13 2351
 
4.2%
19 2329
 
4.2%
20 2198
 
3.9%
12 2181
 
3.9%
21 2054
 
3.7%
Other values (235) 31473
56.5%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 1
 
< 0.1%
3 4
 
< 0.1%
4 25
 
< 0.1%
5 49
 
0.1%
6 115
 
0.2%
7 272
 
0.5%
8 503
 
0.9%
9 834
1.5%
10 1355
2.4%
ValueCountFrequency (%)
2914 1
< 0.1%
2062 1
< 0.1%
1783 1
< 0.1%
1612 1
< 0.1%
1504 1
< 0.1%
1400 2
< 0.1%
1155 2
< 0.1%
745 2
< 0.1%
740 1
< 0.1%
713 1
< 0.1%

Gtp
Real number (ℝ)

Distinct488
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.952201
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size435.2 KiB
2024-04-03T12:34:40.942490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q117
median25
Q343
95-th percentile112.45
Maximum999
Range998
Interquartile range (IQR)26

Descriptive statistics

Standard deviation50.290539
Coefficient of variation (CV)1.2587677
Kurtosis75.109281
Mean39.952201
Median Absolute Deviation (MAD)10
Skewness6.7447977
Sum2225018
Variance2529.1383
MonotonicityNot monotonic
2024-04-03T12:34:41.099785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2117
 
3.8%
16 2109
 
3.8%
14 2085
 
3.7%
17 2055
 
3.7%
18 2013
 
3.6%
13 1874
 
3.4%
19 1821
 
3.3%
20 1775
 
3.2%
21 1680
 
3.0%
22 1595
 
2.9%
Other values (478) 36568
65.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 15
 
< 0.1%
6 47
 
0.1%
7 119
 
0.2%
8 230
 
0.4%
9 508
0.9%
10 909
1.6%
ValueCountFrequency (%)
999 5
< 0.1%
976 1
 
< 0.1%
961 2
 
< 0.1%
933 2
 
< 0.1%
926 1
 
< 0.1%
910 1
 
< 0.1%
894 1
 
< 0.1%
875 1
 
< 0.1%
873 1
 
< 0.1%
850 1
 
< 0.1%

oral
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
True
55692 
ValueCountFrequency (%)
True 55692
100.0%
2024-04-03T12:34:41.136017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

dental caries
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.2 KiB
0
43811 
1
11881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters55692
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43811
78.7%
1 11881
 
21.3%

Length

2024-04-03T12:34:41.171817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T12:34:41.210201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43811
78.7%
1 11881
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 43811
78.7%
1 11881
 
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55692
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43811
78.7%
1 11881
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 55692
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43811
78.7%
1 11881
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43811
78.7%
1 11881
 
21.3%

tartar
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
True
30940 
False
24752 
ValueCountFrequency (%)
True 30940
55.6%
False 24752
44.4%
2024-04-03T12:34:41.241691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

smoking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size435.2 KiB
0
35237 
1
20455 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters55692
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35237
63.3%
1 20455
36.7%

Length

2024-04-03T12:34:41.282856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T12:34:41.314856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 35237
63.3%
1 20455
36.7%

Most occurring characters

ValueCountFrequency (%)
0 35237
63.3%
1 20455
36.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55692
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35237
63.3%
1 20455
36.7%

Most occurring scripts

ValueCountFrequency (%)
Common 55692
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35237
63.3%
1 20455
36.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35237
63.3%
1 20455
36.7%

Interactions

2024-04-03T12:34:37.244798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.355632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.415586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.300493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.213190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.178502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.096779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.058345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.930692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.859455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.740535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.714020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.657871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.558029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.548365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.463064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.429318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.404480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.330682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.232851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.286315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.445435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.462254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.343234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.256801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.227334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.136553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.098585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.976412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.907890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.786107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.761831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.699259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.600286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.592580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.520827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.470508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.451626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.370006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.278614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.330338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.516426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.507300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.384423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.388796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.272268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.186169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.139597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.023865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.949552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.828141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.810263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.741331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.645418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.633821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.566811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.516292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.502181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.416978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.319190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.376993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.695585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.551349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.429281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.433062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.317087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.230204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.179942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.067971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.989867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.870112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.867356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.787265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.693309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.674517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.611605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.666316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.554992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.461610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.367670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.419099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.737753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.590675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.472396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.475916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.361822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.271205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.221957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.107892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.030477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.912129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.911405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.833353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.735237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.725970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.651313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.705347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.596798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.506772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.412726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.463585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.778549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.639612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.516428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.515834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.405718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.324615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.271564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.163090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.072359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.054595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.958638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.881751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.783684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.772617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.706650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.747943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.640629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.554367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.462212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.504462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.824385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.684965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.560006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.555683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.449526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.367168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.317698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.205065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.114273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.098744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.999240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.922217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.823191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.817994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.750449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.797002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.680972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.601520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.599161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.547549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:19.861932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.729874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.599922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.597478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.497565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.411271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.358192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-04-03T12:34:25.796478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.726602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.604458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.576491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.518741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.422409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.410233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.332378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.294665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.274603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.193078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.094081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.100896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:38.051104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.329950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.213992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.125794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.075228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.007256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.889204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.842353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.765397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.647379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.623776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.560646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.465369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.453908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.376678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.343075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.318828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.238947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.144802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.151136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:38.095652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:20.372139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:21.256527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:22.170579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:23.122342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:24.050780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.020675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:25.888461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:26.816615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:27.698899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:28.669289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:29.610322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:30.514354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:31.500188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:32.420812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:33.383724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:34.362221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:35.287976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:36.187838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T12:34:37.197381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-03T12:34:38.180227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-03T12:34:38.390387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDgenderageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)hearing(left)hearing(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtporaldental cariestartarsmoking
00F401556081.31.21.01.01.0114.073.094.0215.082.073.0126.012.91.00.718.019.027.0Y0Y0
11F401606081.00.80.61.01.0119.070.0130.0192.0115.042.0127.012.71.00.622.019.018.0Y0Y0
22M551706080.00.80.81.01.0138.086.089.0242.0182.055.0151.015.81.01.021.016.022.0Y0N1
33M401657088.01.51.51.01.0100.060.096.0322.0254.045.0226.014.71.01.019.026.018.0Y0Y0
44F401556086.01.01.01.01.0120.074.080.0184.074.062.0107.012.51.00.616.014.022.0Y0N0
55M301807585.01.21.21.01.0128.076.095.0217.0199.048.0129.016.21.01.218.027.033.0Y0Y0
66M401606085.51.01.01.01.0116.082.094.0226.068.055.0157.017.01.00.721.027.039.0Y1Y1
77M451659096.01.21.01.01.0153.096.0158.0222.0269.034.0134.015.01.01.338.071.0111.0Y0Y0
89F501506085.00.70.81.01.0115.074.086.0210.066.048.0149.013.71.00.831.031.014.0Y0N0
910M451757589.01.01.01.01.0113.064.094.0198.0147.043.0126.016.01.00.826.024.063.0Y0N0
IDgenderageheight(cm)weight(kg)waist(cm)eyesight(left)eyesight(right)hearing(left)hearing(right)systolicrelaxationfasting blood sugarCholesteroltriglycerideHDLLDLhemoglobinUrine proteinserum creatinineASTALTGtporaldental cariestartarsmoking
5568255655M201757585.00.91.51.01.0118.072.080.0167.0167.053.080.016.61.01.213.06.014.0Y0Y0
5568355663M401808586.51.21.21.01.0116.069.096.0289.0150.068.0183.016.31.01.321.019.038.0Y0N0
5568455666M401706585.01.21.21.01.0106.069.085.0192.0162.044.0116.015.61.01.122.025.033.0Y0Y1
5568555671M401708090.51.21.51.01.0130.084.091.0216.0121.057.0135.014.81.00.916.028.068.0Y0Y0
5568655673F601505075.01.01.21.01.0102.060.085.0179.053.052.0116.012.61.00.826.021.014.0Y0Y0
5568755676F401706575.00.90.91.01.0110.068.089.0213.099.075.0118.012.31.00.614.07.010.0Y1Y0
5568855681F451605070.01.21.21.01.0101.062.089.0166.069.073.079.014.01.00.920.012.014.0Y0Y0
5568955683F551605068.51.01.21.01.0117.072.088.0158.077.079.063.012.41.00.517.011.012.0Y0N0
5569055684M601656078.00.81.01.01.0133.076.0107.0210.079.048.0146.014.41.00.720.019.018.0Y0N0
5569155691M551606585.00.90.71.01.0124.075.082.0213.0142.034.0150.015.01.00.826.029.041.0Y0Y1